2019 U.S. Emerging Jobs Report

In the third annual LinkedIn Emerging Jobs Report, AI Specialist took
the number one spot, with 74% annual growth in hiring for this role in
the past four years. Below, Romer Rosales, Senior Director of Artificial
Intelligence at LinkedIn, shares skills that are important for AI
Specialists and thoughts on the massive growth of this position.

Skills for AI Specialists

The are a number of important education & skill foundations for people who want to become AI Specialists. Assuming that a student already has a solid foundation in math and algorithms, three areas that are important to understand and be familiar with as an AI specialist are statistics, information theory, and optimization. When students ask me about subject areas that they should focus on if they want a career in AI, I point them to those three. Most AI problems can be plausibly approached from multiple angles; understanding stats, information theory, and optimization can help guide you to the best way to solve a given problem.

When it comes to applications of AI, one of the most important skills to have is the ability to investigate and understand the domain of the problem you’re trying to solve. For instance, if I am working on AI for a biology project, it will help me if I can learn about important concepts in biology and understand the problem I’m trying to solve from that perspective.

Being able to exchange ideas with an expert in the industry where you are working is something that is underappreciated by many otherwise good AI engineers. Knowing the key algorithms isn’t the only thing that’s important—you need the extra ability to understand new problem domains not in your area of expertise. Likewise, good AI engineers also develop communication skills to be able to explain their work to people who aren’t AI experts, which aids the exchange of ideas and fosters a more collaborative environment.

Growing AI Teams

It is important to understand how companies can approach growing their AI teams or growing the expertise in their companies. We are learning more every day about new ways to use AI to solve different, real-life problems. Companies are realizing that this is a core skill they need to have, even if it’s a small team, and this realization is what’s fueling the demand for AI skills and, more generally, data analytics skills.

The benefit of increasing the base level of AI understanding across a company—or even the general public—is that it makes it possible to have more productive and informed conversations about everything from AI in product design to the social implications of the technology.

Having programs like LinkedIn’s AI Academy to teach people more about the basics of AI is important, but it doesn’t replace the need for AI specialists. But like any science or area of expertise, it requires a lot of training and experience to get to the point of becoming an AI expert.

Resource Links:

Industry Perspectives

In this special guest feature, Sean McDermott, CEO and founder of Windward Consulting Group and RedMonocle, offers what enterprises need to know about the five levels of AIOps maturity. When maneuvering through each level, keep the long-term AIOps strategy and goals at the center to achieve the true potential of AIOps.

Latest Video

White Papers

In this short eBook, you’ll discover automated machine learning using H2O.ai. H2O.ai has dedicated itself to democratizing all aspects of AI, including machine
learning. H2O Driverless AI is a machine learning solution that automates AI for nontechnical
users. So-called “AutoML” solutions like H2O Driverless AI are rising in popularity for enterprises across a wide range of industries. With it, users can build robust, fast, and accurate machine learning solutions. It also includes visualization and interpretability features that explain the data modeling results in plain English, fostering further adoption and trust in AI.